def PrintChiChannels(DataDirectory,
fname_prefix,
ChannelFileName,
add_basin_labels=True,
cmap="jet",
cbar_loc="right",
size_format="ESURF",
fig_format="png",
dpi=250,
plotting_column="source_key",
discrete_colours=False,
NColours=10,
out_fname_prefix=""):
"""
This function prints a channel map over a hillshade.
Args:
DataDirectory (str): the data directory with the m/n csv files
fname_prefix (str): The prefix for the m/n csv files
ChannelFileName (str): The name of the channel file (a csv) without path but with extension
add_basin_labels (bool): If true, label the basins with text. Otherwise use a colourbar.
cmap (str or colourmap): The colourmap to use for the plot
cbar_lox (str): where you want the colourbar. Options are none, left, right, top and botton. The colourbar will be of the elevation.
If you want only a hillshade set to none and the cmap to "gray"
size_format (str): Either geomorphology or big. Anything else gets you a 4.9 inch wide figure (standard ESURF size)
fig_format (str): An image format. png, pdf, eps, svg all valid
dpi (int): The dots per inch of the figure
plotting_column (str): the name of the column to plot
discrete_colours (bool): if true use a discrete colourmap
NColours (int): the number of colours to cycle through when making the colourmap
out_fname_prefix (str): The prefix of the image file. If blank uses the fname_prefix
Returns:
Shaded relief plot with the channels coloured by a plotting column designated by the plotting_column keyword. Uses a colourbar to show each basin
Author: SMM
"""
# specify the figure size and format
# set figure sizes based on format
if size_format == "geomorphology":
fig_size_inches = 6.25
elif size_format == "big":
fig_size_inches = 16
else:
fig_size_inches = 4.92126
ax_style = "Normal"
# Get the filenames you want
BackgroundRasterName = fname_prefix + "_hs.bil"
DrapeRasterName = fname_prefix + ".bil"
chi_csv_fname = DataDirectory + ChannelFileName
thisPointData = LSDMap_PD.LSDMap_PointData(chi_csv_fname)
# clear the plot
plt.clf()
# set up the base image and the map
MF = MapFigure(BackgroundRasterName,
DataDirectory,
coord_type="UTM_km",
colourbar_location="None")
MF.add_drape_image(DrapeRasterName,
DataDirectory,
colourmap="gray",
alpha=0.6)
MF.add_point_data(thisPointData,
column_for_plotting=plotting_column,
this_colourmap=cmap,
scale_points=True,
column_for_scaling="drainage_area",
scaled_data_in_log=True,
max_point_size=5,
min_point_size=1,
discrete_colours=discrete_colours,
NColours=NColours)
# Save the image
if len(out_fname_prefix) == 0:
ImageName = DataDirectory + fname_prefix + "_chi_channels." + fig_format
else:
ImageName = DataDirectory + out_fname_prefix + "_chi_channels." + fig_format
MF.save_fig(fig_width_inches=fig_size_inches,
FigFileName=ImageName,
axis_style=ax_style,
FigFormat=fig_format,
Fig_dpi=dpi)
def PrintBasins_Complex(DataDirectory,
fname_prefix,
use_keys_not_junctions=True,
show_colourbar=False,
Remove_Basins=[],
Rename_Basins={},
Value_dict={},
cmap="jet",
colorbarlabel="colourbar",
size_format="ESURF",
fig_format="png",
dpi=250,
out_fname_prefix="",
include_channels=False,
label_basins=True,
save_fig=True):
"""
This function makes a shaded relief plot of the DEM with the basins coloured
by the basin ID.
Args:
DataDirectory (str): the data directory with the m/n csv files
fname_prefix (str): The prefix for the m/n csv files
use_keys_not_junctions (bool): If true use basin keys to locate basins, otherwise use junction indices
show_colourbar (bool): if true show the colourbar
Remove_Basins (list): A lists containing either key or junction indices of basins you want to remove from plotting
Rename_Basins (dict): A dict where the key is either basin key or junction index, and the value is a new name for the basin denoted by the key
Value_dict (dict): A dict where the key is either basin key or junction index, and the value is a value of the basin that is used to colour the basins
add_basin_labels (bool): If true, label the basins with text. Otherwise use a colourbar.
cmap (str or colourmap): The colourmap to use for the plot
cbar_loc (str): where you want the colourbar. Options are none, left, right, top and botton. The colourbar will be of the elevation.
If you want only a hillshade set to none and the cmap to "gray"
size_format (str): Either geomorphology or big. Anything else gets you a 4.9 inch wide figure (standard ESURF size)
fig_format (str): An image format. png, pdf, eps, svg all valid
dpi (int): The dots per inch of the figure
out_fname_prefix (str): The prefix of the image file. If blank uses the fname_prefix
include_channels (bool): If true, adds a channel plot. It uses the chi_data_maps file
label_basins (bool): If true, the basins get labels
Returns:
A string with the name of the image (printed to file): Shaded relief plot with the basins coloured by basin ID. Uses a colourbar to show each basin. This allows more complex plotting with renamed and excluded basins.
Author: FJC, SMM
"""
# set figure sizes based on format
if size_format == "geomorphology":
fig_width_inches = 6.25
elif size_format == "big":
fig_width_inches = 16
else:
fig_width_inches = 4.92126
# get the basin IDs to make a discrete colourmap for each ID
BasinInfoDF = PlotHelp.ReadBasinInfoCSV(DataDirectory, fname_prefix)
basin_keys = list(BasinInfoDF['basin_key'])
basin_keys = [int(x) for x in basin_keys]
basin_junctions = list(BasinInfoDF['outlet_junction'])
basin_junctions = [float(x) for x in basin_junctions]
print('Basin keys are: ')
print(basin_keys)
# going to make the basin plots - need to have bil extensions.
print(
"I'm going to make the basin plots. Your topographic data must be in ENVI bil format or I'll break!!"
)
# clear the plot
plt.clf()
# get the rasters
raster_ext = '.bil'
#BackgroundRasterName = fname_prefix+raster_ext
HillshadeName = fname_prefix + '_hs' + raster_ext
BasinsName = fname_prefix + '_AllBasins' + raster_ext
# This initiates the figure
MF = MapFigure(HillshadeName,
DataDirectory,
coord_type="UTM_km",
colourbar_location="None")
# This adds the basins
MF.add_basin_plot(BasinsName,
fname_prefix,
DataDirectory,
mask_list=Remove_Basins,
rename_dict=Rename_Basins,
value_dict=Value_dict,
use_keys_not_junctions=use_keys_not_junctions,
show_colourbar=show_colourbar,
discrete_cmap=True,
n_colours=15,
colorbarlabel=colorbarlabel,
colourmap=cmap,
adjust_text=False,
label_basins=label_basins)
# See if you need the channels
if include_channels:
print("I am going to add some channels for you")
ChannelFileName = fname_prefix + "_chi_data_map.csv"
chi_csv_fname = DataDirectory + ChannelFileName
thisPointData = LSDP.LSDMap_PointData(chi_csv_fname)
MF.add_point_data(thisPointData,
column_for_plotting="basin_key",
scale_points=True,
column_for_scaling="drainage_area",
this_colourmap="Blues_r",
scaled_data_in_log=True,
max_point_size=3,
min_point_size=1,
discrete_colours=True,
NColours=1,
zorder=5)
# Save the image
thing_to_return = []
if (save_fig):
if len(out_fname_prefix) == 0:
ImageName = DataDirectory + fname_prefix + "_selected_basins." + fig_format
else:
ImageName = DataDirectory + out_fname_prefix + "_selected_basins." + fig_format
MF.save_fig(fig_width_inches=fig_width_inches,
FigFileName=ImageName,
FigFormat=fig_format,
Fig_dpi=dpi,
transparent=True)
thing_to_return = ImageName
else:
fig = MF.save_fig(fig_width_inches=fig_width_inches,
transparent=True,
return_fig=True)
thing_to_return = fig
print("I'm returning:")
print(thing_to_return)
return thing_to_return
def PrintChiCoordChannelsAndBasins(DataDirectory,
fname_prefix,
ChannelFileName,
add_basin_labels=True,
cmap="cubehelix",
cbar_loc="right",
size_format="ESURF",
fig_format="png",
dpi=250,
plotting_column="source_key",
discrete_colours=False,
NColours=10,
colour_log=True,
colorbarlabel="Colourbar",
Basin_remove_list=[],
Basin_rename_dict={},
value_dict={},
plot_chi_raster=False,
out_fname_prefix="",
show_basins=True,
min_channel_point_size=0.5,
max_channel_point_size=2):
"""
This function prints a channel map and has the option of plooting over a raster of chi values. Similar to PrintChiChannelsAndBasins but adds the map of chi coordinate underneath
Args:
DataDirectory (str): the data directory with the m/n csv files
fname_prefix (str): The prefix for the m/n csv files
add_basin_labels (bool): If true, label the basins with text. Otherwise use a colourbar.
cmap (str or colourmap): The colourmap to use for the plot
cbar_loc (str): where you want the colourbar. Options are none, left, right, top and botton. The colourbar will be of the elevation.
If you want only a hillshade set to none and the cmap to "gray"
size_format (str): Either geomorphology or big. Anything else gets you a 4.9 inch wide figure (standard ESURF size)
fig_format (str): An image format. png, pdf, eps, svg all valid
dpi (int): The dots per inch of the figure
plotting_column (str): the name of the column to plot
discrete_colours (bool): if true use a discrete colourmap
NColours (int): the number of colours to cycle through when making the colourmap
colour_log (bool): If true the colours are in log scale
Basin_remove_list (list): A lists containing either key or junction indices of basins you want to remove from plotting
Basin_rename_dict (dict): A dict where the key is either basin key or junction index, and the value is a new name for the basin denoted by the key
Value_dict (dict): A dict where the key is either basin key or junction index, and the value is a value of the basin that is used to colour the basins
plot_chi_raster (bool): finds the chi raster and plots this underneath the chi points in the channels. It looks nice.
out_fname_prefix (str): The prefix of the image file. If blank uses the fname_prefix
show_basins (bool): If true, plot the basins
min_channel_point_size (float): The minimum size of a channel point in points
max_channel_point_size (float): The maximum size of a channel point in points
Returns:
Shaded relief plot with the basins coloured by basin ID. Includes channels. These can be plotted by various metrics denoted but the plotting_column parameter.
Author: SMM
"""
# specify the figure size and format
# set figure sizes based on format
if size_format == "geomorphology":
fig_size_inches = 6.25
elif size_format == "big":
fig_size_inches = 16
else:
fig_size_inches = 4.92126
ax_style = "Normal"
# get the basin IDs to make a discrete colourmap for each ID
BasinInfoDF = PlotHelp.ReadBasinInfoCSV(DataDirectory, fname_prefix)
basin_keys = list(BasinInfoDF['basin_key'])
basin_keys = [int(x) for x in basin_keys]
basin_junctions = list(BasinInfoDF['outlet_junction'])
basin_junctions = [float(x) for x in basin_junctions]
print('Basin keys are: ')
print(basin_keys)
# going to make the basin plots - need to have bil extensions.
print(
"I'm going to make the basin plots. Your topographic data must be in ENVI bil format or I'll break!!"
)
# get the rasters
raster_ext = '.bil'
#BackgroundRasterName = fname_prefix+raster_ext
HillshadeName = fname_prefix + '_hs' + raster_ext
BasinsName = fname_prefix + '_AllBasins' + raster_ext
ChiCoordName = fname_prefix + '_Maskedchi' + raster_ext
print(BasinsName)
Basins = LSDV.GetBasinOutlines(DataDirectory, BasinsName)
chi_csv_fname = DataDirectory + ChannelFileName
chi_csv_fname = DataDirectory + ChannelFileName
thisPointData = LSDMap_PD.LSDMap_PointData(chi_csv_fname)
# Remove data that has nodata values
thisPointData.selectValue(plotting_column, value=-9999, operator="!=")
thisPointData.selectValue("basin_key",
value=Basin_remove_list,
operator="!=")
#print("The new point data is:")
#print(thisPointData.GetLongitude())
# clear the plot
plt.clf()
# set up the base image and the map
print("I am showing the basins without text labels.")
MF = MapFigure(HillshadeName,
DataDirectory,
coord_type="UTM_km",
colourbar_location="None")
# This adds the basins
if plot_chi_raster:
if show_basins:
MF.add_basin_plot(BasinsName,
fname_prefix,
DataDirectory,
mask_list=Basin_remove_list,
rename_dict=Basin_rename_dict,
value_dict=value_dict,
label_basins=add_basin_labels,
show_colourbar=False,
colourmap="gray",
alpha=1,
outlines_only=True)
MF.add_drape_image(ChiCoordName,
DataDirectory,
colourmap="cubehelix",
alpha=0.6,
zorder=0.5)
MF.add_point_data(thisPointData,
column_for_plotting=plotting_column,
scale_points=True,
column_for_scaling="drainage_area",
show_colourbar=True,
colourbar_location=cbar_loc,
colorbarlabel=colorbarlabel,
this_colourmap=cmap,
scaled_data_in_log=True,
max_point_size=max_channel_point_size,
min_point_size=min_channel_point_size,
zorder=0.4,
colour_log=colour_log,
discrete_colours=discrete_colours,
NColours=NColours)
else:
if show_basins:
MF.add_basin_plot(BasinsName,
fname_prefix,
DataDirectory,
mask_list=Basin_remove_list,
rename_dict=Basin_rename_dict,
value_dict=value_dict,
label_basins=add_basin_labels,
show_colourbar=False,
colourmap="gray",
alpha=0.7,
outlines_only=False)
MF.add_point_data(thisPointData,
column_for_plotting=plotting_column,
scale_points=True,
column_for_scaling="drainage_area",
show_colourbar=True,
colourbar_location=cbar_loc,
colorbarlabel=colorbarlabel,
this_colourmap=cmap,
scaled_data_in_log=True,
max_point_size=2,
min_point_size=0.5,
zorder=10,
colour_log=colour_log,
discrete_colours=discrete_colours,
NColours=NColours)
# Save the image
if len(out_fname_prefix) == 0:
ImageName = DataDirectory + fname_prefix + "_chicoord_and_basins." + fig_format
else:
ImageName = DataDirectory + out_fname_prefix + "_chicoord_and_basins." + fig_format
MF.save_fig(fig_width_inches=fig_size_inches,
FigFileName=ImageName,
axis_style=ax_style,
FigFormat=fig_format,
Fig_dpi=dpi)
def PrintChannelsAndBasins(DataDirectory,
fname_prefix,
add_basin_labels=True,
cmap="jet",
size_format="ESURF",
fig_format="png",
dpi=250,
out_fname_prefix="",
save_fig=True):
"""
This function prints a channel map over a hillshade.
Args:
DataDirectory (str): the data directory with the m/n csv files
fname_prefix (str): The prefix for the m/n csv files
add_basin_labels (bool): If true, label the basins with text. Otherwise use a colourbar.
cmap (str or colourmap): The colourmap to use for the plot
size_format (str): Either geomorphology or big. Anything else gets you a 4.9 inch wide figure (standard ESURF size)
fig_format (str): An image format. png, pdf, eps, svg all valid
dpi (int): The dots per inch of the figure
out_fname_prefix (str): The prefix of the image file. If blank uses the fname_prefix
Returns:
A string with the name of the image (printed to file): Shaded relief plot with the basins coloured by basin ID. Uses a colourbar to show each basin
Author: SMM
"""
# specify the figure size and format
# set figure sizes based on format
if size_format == "geomorphology":
fig_size_inches = 6.25
elif size_format == "big":
fig_size_inches = 16
else:
fig_size_inches = 4.92126
ax_style = "Normal"
# get the basin IDs to make a discrete colourmap for each ID
BasinInfoDF = PlotHelp.ReadBasinInfoCSV(DataDirectory, fname_prefix)
basin_keys = list(BasinInfoDF['basin_key'])
basin_keys = [int(x) for x in basin_keys]
basin_junctions = list(BasinInfoDF['outlet_junction'])
basin_junctions = [float(x) for x in basin_junctions]
print('Basin keys are: ')
print(basin_keys)
# going to make the basin plots - need to have bil extensions.
print(
"I'm going to make the basin plots. Your topographic data must be in ENVI bil format or I'll break!!"
)
# get the rasters
raster_ext = '.bil'
#BackgroundRasterName = fname_prefix+raster_ext
HillshadeName = fname_prefix + '_hs' + raster_ext
BasinsName = fname_prefix + '_AllBasins' + raster_ext
print(BasinsName)
Basins = LSDV.GetBasinOutlines(DataDirectory, BasinsName)
ChannelFileName = fname_prefix + "_chi_data_map.csv"
chi_csv_fname = DataDirectory + ChannelFileName
thisPointData = LSDP.LSDMap_PointData(chi_csv_fname)
# clear the plot
plt.clf()
# set up the base image and the map
print("I am showing the basins without text labels.")
MF = MapFigure(HillshadeName,
DataDirectory,
coord_type="UTM_km",
colourbar_location="None")
MF.plot_polygon_outlines(Basins, linewidth=0.8)
MF.add_drape_image(BasinsName,
DataDirectory,
colourmap=cmap,
alpha=0.1,
discrete_cmap=False,
n_colours=len(basin_keys),
show_colourbar=False,
modify_raster_values=True,
old_values=basin_junctions,
new_values=basin_keys,
cbar_type=int)
MF.add_point_data(thisPointData,
column_for_plotting="basin_key",
scale_points=True,
column_for_scaling="drainage_area",
this_colourmap=cmap,
scaled_data_in_log=True,
max_point_size=3,
min_point_size=1)
# Save the image
thing_to_return = []
if (save_fig):
if len(out_fname_prefix) == 0:
ImageName = DataDirectory + fname_prefix + "_channels_with_basins." + fig_format
else:
ImageName = DataDirectory + out_fname_prefix + "_channels_with_basins." + fig_format
MF.save_fig(fig_width_inches=fig_size_inches,
FigFileName=ImageName,
FigFormat=fig_format,
Fig_dpi=dpi,
transparent=True)
thing_to_return = ImageName
else:
fig = MF.save_fig(fig_width_inches=fig_size_inches,
transparent=True,
return_fig=True)
thing_to_return = fig
print("I'm returning:")
print(thing_to_return)
return thing_to_return
def PrintBasins(DataDirectory,
fname_prefix,
add_basin_labels=True,
cmap="jet",
cbar_loc="right",
size_format="ESURF",
fig_format="png",
dpi=250,
out_fname_prefix=""):
"""
This function makes a shaded relief plot of the DEM with the basins coloured
by the basin ID.
IMPORTANT: To get this to run you need to set the flags in chi mapping tool to:
write_hillshade: true
print_basin_raster: true
print_chi_data_maps: true
Args:
DataDirectory (str): the data directory with the m/n csv files
fname_prefix (str): The prefix for the m/n csv files
add_basin_labels (bool): If true, label the basins with text. Otherwise use a colourbar.
cmap (str or colourmap): The colourmap to use for the plot
cbar_loc (str): where you want the colourbar. Options are none, left, right, top and botton. The colourbar will be of the elevation.
If you want only a hillshade set to none and the cmap to "gray"
size_format (str): Either geomorphology or big. Anything else gets you a 4.9 inch wide figure (standard ESURF size)
fig_format (str): An image format. png, pdf, eps, svg all valid
dpi (int): The dots per inch of the figure
out_fname_prefix (str): The prefix of the image file. If blank uses the fname_prefix
Returns:
A string with the name of the image (printed to file): Shaded relief plot with the basins coloured by basin ID. Uses a colourbar to show each basin
Author: FJC, SMM
"""
# set figure sizes based on format
if size_format == "geomorphology":
fig_width_inches = 6.25
elif size_format == "big":
fig_width_inches = 16
else:
fig_width_inches = 4.92126
# get the basin IDs to make a discrete colourmap for each ID
BasinInfoDF = PlotHelp.ReadBasinInfoCSV(DataDirectory, fname_prefix)
basin_keys = list(BasinInfoDF['basin_key'])
basin_keys = [int(x) for x in basin_keys]
basin_junctions = list(BasinInfoDF['outlet_junction'])
basin_junctions = [float(x) for x in basin_junctions]
print('Basin keys are: ')
print(basin_keys)
# going to make the basin plots - need to have bil extensions.
print(
"I'm going to make the basin plots. Your topographic data must be in ENVI bil format or I'll break!!"
)
# clear the plot
plt.clf()
# get the rasters
raster_ext = '.bil'
#BackgroundRasterName = fname_prefix+raster_ext
HillshadeName = fname_prefix + '_hs' + raster_ext
BasinsName = fname_prefix + '_AllBasins' + raster_ext
print(BasinsName)
Basins = LSDV.GetBasinOutlines(DataDirectory, BasinsName)
# If wanted, add the labels
if add_basin_labels:
print("I am going to add basin labels, there will be no colourbar.")
MF = MapFigure(HillshadeName,
DataDirectory,
coord_type="UTM_km",
colourbar_location="None")
MF.plot_polygon_outlines(Basins, linewidth=0.8)
MF.add_drape_image(BasinsName,
DataDirectory,
colourmap=cmap,
alpha=0.8,
colorbarlabel='Basin ID',
discrete_cmap=True,
n_colours=len(basin_keys),
show_colourbar=False,
modify_raster_values=True,
old_values=basin_junctions,
new_values=basin_keys,
cbar_type=int)
# This is used to label the basins
label_dict = dict(zip(basin_junctions, basin_keys))
# this dict has the basin junction as the key and the basin_key as the value
Points = LSDV.GetPointWithinBasins(DataDirectory, BasinsName)
MF.add_text_annotation_from_shapely_points(Points,
text_colour='k',
label_dict=label_dict)
else:
print("I am showing the basins without text labels.")
MF = MapFigure(HillshadeName,
DataDirectory,
coord_type="UTM_km",
colourbar_location=cbar_loc)
MF.plot_polygon_outlines(Basins, linewidth=0.8)
MF.add_drape_image(BasinsName,
DataDirectory,
colourmap=cmap,
alpha=0.8,
colorbarlabel='Basin ID',
discrete_cmap=True,
n_colours=len(basin_keys),
show_colourbar=True,
modify_raster_values=True,
old_values=basin_junctions,
new_values=basin_keys,
cbar_type=int)
# Save the image
thing_to_return = []
if (save_fig):
if len(out_fname_prefix) == 0:
ImageName = DataDirectory + fname_prefix + "_basins." + fig_format
else:
ImageName = DataDirectory + out_fname_prefix + "_basins." + fig_format
MF.save_fig(fig_width_inches=fig_width_inches,
FigFileName=ImageName,
FigFormat=fig_format,
Fig_dpi=dpi,
transparent=True)
thing_to_return = ImageName
else:
fig = MF.save_fig(fig_width_inches=fig_width_inches,
transparent=True,
return_fig=True)
thing_to_return = fig
print("I'm returning:")
print(thing_to_return)
return thing_to_return
def PrintChannels(DataDirectory,
fname_prefix,
ChannelFileName,
cmap="jet",
size_format="ESURF",
fig_format="png",
dpi=250,
out_fname_prefix="",
plotting_column="basin_key",
save_fig=True):
"""
This function prints a channel map over a hillshade. It is more flexible than PrintAllChannels since you can choose the channel csv and you can also choose the column in the csv to plot
Args:
DataDirectory (str): the data directory with the m/n csv files
fname_prefix (str): The prefix for the m/n csv files
ChannelFileName (str): The name of the channel file. Doesn't need path but needs extension
cmap (str or colourmap): The colourmap to use for the plot
size_format (str): Either geomorphology or big. Anything else gets you a 4.9 inch wide figure (standard ESURF size)
fig_format (str): An image format. png, pdf, eps, svg all valid
dpi (int): The dots per inch of the figure
out_fname_prefix (str): The prefix of the image file. If blank uses the fname_prefix
plotting_column (str): the column to plot from the csv file
save_fig (bool): If true, saves the fig, else, returns the filename
Returns:
If save_fig is true, return a string with the name of the image (printed to file). If save_fig is false, returns the figure handle to the shaded relief plot with the channels.
Returns:
A string with the name of the image (printed to file): A string with the name of the image (printed to file): Shaded relief plot with the basins coloured by basin ID. Uses a colourbar to show each basin
Author: SMM
"""
# specify the figure size and format
# set figure sizes based on format
if size_format == "geomorphology":
fig_size_inches = 6.25
elif size_format == "big":
fig_size_inches = 16
else:
fig_size_inches = 4.92126
ax_style = "Normal"
# Get the filenames you want
BackgroundRasterName = fname_prefix + "_hs.bil"
DrapeRasterName = fname_prefix + ".bil"
chi_csv_fname = DataDirectory + ChannelFileName
print("Let me get the point data")
print("The filename is: " + chi_csv_fname)
thisPointData = LSDP.LSDMap_PointData(chi_csv_fname)
print("The parameter names are")
thisPointData.GetParameterNames(True)
# clear the plot
plt.clf()
# set up the base image and the map
MF = MapFigure(BackgroundRasterName,
DataDirectory,
coord_type="UTM_km",
colourbar_location="None")
MF.add_drape_image(DrapeRasterName,
DataDirectory,
colourmap="gray",
alpha=0.6)
MF.add_point_data(thisPointData,
column_for_plotting=plotting_column,
scale_points=True,
column_for_scaling="drainage_area",
this_colourmap=cmap,
scaled_data_in_log=True,
max_point_size=5,
min_point_size=1)
# Save the image
thing_to_return = []
if (save_fig):
if len(out_fname_prefix) == 0:
ImageName = DataDirectory + fname_prefix + "_channels_coloured_by_basin." + fig_format
else:
ImageName = DataDirectory + out_fname_prefix + "_channels_coloured_by_basin." + fig_format
MF.save_fig(fig_width_inches=fig_size_inches,
FigFileName=ImageName,
FigFormat=fig_format,
Fig_dpi=dpi,
transparent=True)
thing_to_return = ImageName
else:
fig = MF.save_fig(fig_width_inches=fig_size_inches,
transparent=True,
return_fig=True)
thing_to_return = fig
print("I'm returning:")
print(thing_to_return)
return thing_to_return
def SimpleHillshade(DataDirectory,
Base_file,
cmap="terrain",
cbar_loc="right",
size_format="ESURF",
fig_format="png",
dpi=250,
out_fname_prefix="",
save_fig=True):
"""
This function makes a shaded relief plot of the DEM.
Args:
DataDirectory (str): the data directory with the rasters
Base_file (str): The prefix for the rasters
cmap (str or colourmap): The colourmap to use for the plot
cbar_loc (str): where you want the colourbar. Options are none, left, right, top and botton. The colourbar will be of the elevation.
If you want only a hillshade set to none and the cmap to "gray"
size_format (str): Either geomorphology or big. Anything else gets you a 4.9 inch wide figure (standard ESURF size)
fig_format (str): An image format. png, pdf, eps, svg all valid
dpi (int): The dots per inch of the figure
out_fname_prefix (str): The prefix of the image file. If blank uses the fname_prefix
save_fig (bool): If true, saves the fig, else, returns the filename
Returns:
If save_fig is true, return a string with the name of the image (printed to file). If save_fig is false, returns the figure handle to the hillshade plot
Author: FJC, SMM
"""
# specify the figure size and format
# set figure sizes based on format
if size_format == "geomorphology":
fig_size_inches = 6.25
elif size_format == "big":
fig_size_inches = 16
else:
fig_size_inches = 4.92126
ax_style = "Normal"
# Get the filenames you want
BackgroundRasterName = Base_file + "_hs.bil"
DrapeRasterName = Base_file + ".bil"
# clear the plot
plt.clf()
# set up the base image and the map
MF = MapFigure(BackgroundRasterName,
DataDirectory,
coord_type="UTM_km",
colourbar_location=cbar_loc)
MF.add_drape_image(DrapeRasterName,
DataDirectory,
colourmap=cmap,
alpha=0.6,
colorbarlabel="Elevation (m)")
# Save the image or return the figure handle
if (save_fig):
if len(out_fname_prefix) == 0:
ImageName = DataDirectory + Base_file + "_hillshade." + fig_format
else:
ImageName = DataDirectory + out_fname_prefix + "_hillshade." + fig_format
MF.save_fig(fig_width_inches=fig_size_inches,
FigFileName=ImageName,
FigFormat=fig_format,
Fig_dpi=dpi,
transparent=True)
thing_to_return = ImageName
else:
fig = MF.save_fig(fig_width_inches=fig_size_inches,
transparent=True,
return_fig=True)
thing_to_return = fig
print("I'm returning:")
print(thing_to_return)
return thing_to_return
def PrintAllChannels(DataDirectory,
fname_prefix,
add_basin_labels=True,
cmap="jet",
cbar_loc="right",
size_format="ESURF",
fig_format="png",
dpi=250,
out_fname_prefix="",
channel_colourmap="Blues",
save_fig=True):
"""
This function prints a channel map over a hillshade. It gets ALL the channels within the DEM: it automatically selects the _CN csv file that is obtained by the print channel network tool in lsdtopotools. Channels are coloured by the stream order.
Args:
DataDirectory (str): the data directory with the m/n csv files
fname_prefix (str): The prefix for the m/n csv files
add_basin_labels (bool): If true, label the basins with text. Otherwise use a colourbar.
cmap (str or colourmap): The colourmap to use for the plot
cbar_lox (str): where you want the colourbar. Options are none, left, right, top and botton. The colourbar will be of the elevation.
If you want only a hillshade set to none and the cmap to "gray"
size_format (str): Either geomorphology or big. Anything else gets you a 4.9 inch wide figure (standard ESURF size)
fig_format (str): An image format. png, pdf, eps, svg all valid
dpi (int): The dots per inch of the figure
out_fname_prefix (str): The prefix of the image file. If blank uses the fname_prefix
channel_colourmap (str or cmap): the colourmap of the point data
save_fig (bool): If true, saves the fig, else, returns the filename
Returns:
If save_fig is true, return a string with the name of the image (printed to file). If save_fig is false, returns the figure handle to the shaded relief plot with the channels.
Author: SMM
"""
# specify the figure size and format
# set figure sizes based on format
if size_format == "geomorphology":
fig_size_inches = 6.25
elif size_format == "big":
fig_size_inches = 16
else:
fig_size_inches = 4.92126
ax_style = "Normal"
# Get the filenames you want
BackgroundRasterName = fname_prefix + "_hs.bil"
DrapeRasterName = fname_prefix + ".bil"
ChannelFileName = fname_prefix + "_CN.csv"
chi_csv_fname = DataDirectory + ChannelFileName
thisPointData = LSDP.LSDMap_PointData(chi_csv_fname)
# clear the plot
plt.clf()
# set up the base image and the map
MF = MapFigure(BackgroundRasterName,
DataDirectory,
coord_type="UTM_km",
colourbar_location="None")
MF.add_drape_image(DrapeRasterName,
DataDirectory,
colourmap=cmap,
alpha=0.6)
MF.add_point_data(thisPointData,
column_for_plotting="Stream Order",
this_colourmap=channel_colourmap,
scale_points=True,
column_for_scaling="Stream Order",
scaled_data_in_log=False,
max_point_size=5,
min_point_size=1,
zorder=10,
alpha=1)
# Save the image
thing_to_return = []
if (save_fig):
if len(out_fname_prefix) == 0:
ImageName = DataDirectory + fname_prefix + "_channels." + fig_format
else:
ImageName = DataDirectory + out_fname_prefix + "_channels." + fig_format
MF.save_fig(fig_width_inches=fig_size_inches,
FigFileName=ImageName,
FigFormat=fig_format,
Fig_dpi=dpi,
transparent=True)
thing_to_return = ImageName
else:
fig = MF.save_fig(fig_width_inches=fig_size_inches,
transparent=True,
return_fig=True)
thing_to_return = fig
print("I'm returning:")
print(thing_to_return)
return thing_to_return
def SimpleHillshadeForAnimation(DataDirectory,
Base_file,
cmap="jet",
cbar_loc="right",
size_format="ESURF",
fig_format="png",
dpi=250,
imgnumber=0,
full_basefile=[],
custom_cbar_min_max=[],
out_fname_prefix="",
coord_type="UTM_km",
hide_ticklabels=False,
save_fig=True):
"""
This function make a hillshade image that is optimised for creating
an animation. Used with the MuddPILE model
Args:
DataDirectory (str): the data directory with the data files
Base_file (str): The prefix for the data files
cmap (str or colourmap): The colourmap to use for the plot
cbar_loc (str): where you want the colourbar. Options are none, left, right, top and botton. The colourbar will be of the elevation.
If you want only a hillshade set to none and the cmap to "gray"
size_format (str): Either geomorphology or big. Anything else gets you a 4.9 inch wide figure (standard ESURF size)
fig_format (str): An image format. png, pdf, eps, svg all valid
dpi (int): The dots per inch of the figure
imgnumber (int): the number of the image. Usually frames from model runs have integer numbers after them
full_basefile (str): The root name of the figures you want. If empty, it uses the data_directory+base_file
custom_min_max (list of int/float): if it contains two elements, recast the raster to [min,max] values for display.
out_fname_prefix (str): The prefix of the image file. If blank uses the fname_prefix
coord_type (str): either UTM or UTM_km
hide_ticklabels (bool): if true, hide the tick labels from the plot
Returns:
A string with the name of the image (printed to file): Shaded relief plot. The elevation is also included in the plot.
Author: FJC, SMM
"""
# specify the figure size and format
# set figure sizes based on format
if size_format == "geomorphology":
fig_size_inches = 6.25
elif size_format == "big":
fig_size_inches = 16
else:
fig_size_inches = 4.92126
ax_style = "Normal"
# Get the filenames you want
BackgroundRasterName = Base_file + "_hs.bil"
DrapeRasterName = Base_file + ".bil"
# clear the plot
plt.clf()
# set up the base image and the map
MF = MapFigure(BackgroundRasterName,
DataDirectory,
coord_type=coord_type,
colourbar_location=cbar_loc)
MF.add_drape_image(DrapeRasterName,
DataDirectory,
colourmap=cmap,
alpha=0.6,
colorbarlabel="Elevation (m)",
colour_min_max=custom_cbar_min_max)
# Save the image
if (full_basefile == []):
if len(out_fname_prefix) == 0:
ImageName = DataDirectory + Base_file + "_img" + "%004d" % (
imgnumber) + "." + fig_format
else:
ImageName = DataDirectory + out_fname_prefix + "_img" + "%004d" % (
imgnumber) + "." + fig_format
else:
ImageName = full_basefile + "_img" + "%004d" % (
imgnumber) + "." + fig_format
MF.save_fig(fig_width_inches=fig_size_inches,
FigFileName=ImageName,
axis_style=ax_style,
FigFormat=fig_format,
Fig_dpi=dpi,
adjust_cbar_characters=False,
fixed_cbar_characters=4,
hide_ticklabels=hide_ticklabels)
return ImageName
def BinaryDrape(DataDirectory,
Base_file,
Drape_prefix,
cmap="Blues",
size_format="ESURF",
fig_format="png",
dpi=250,
out_fname_prefix="",
coord_type="UTM_km",
use_scalebar=False,
save_fig=True,
alpha=0.5):
"""
This function makes a simple drape plot of a binary raster over a hillshade. Raster values with 0 in the binary raster are not displayed, and you can
choose the colour of the values with 1.
Args:
DataDirectory (str): the data directory with the rasters
Base_file (str): The prefix for the rasters
Drape_prefix (str): The prefix of the drape name
colour (str): the colour of the raster values with a value of 1.
size_format (str): Either geomorphology or big. Anything else gets you a 4.9 inch wide figure (standard ESURF size)
fig_format (str): An image format. png, pdf, eps, svg all valid
dpi (int): The dots per inch of the figure
out_fname_prefix (str): The prefix of the image file. If blank uses the fname_prefix
coord_type (str): this can be in UTM_km or UTM_m
use_scalebar (bool): If true inserts a scalebar in the image
save_fig (bool): If true, saves the fig, else, returns the filename
Returns:
If save_fig is true, return a string with the name of the image (printed to file). If save_fig is false, returns the figure handle to the draped plot
Author: FJC, SMM
"""
# specify the figure size and format
# set figure sizes based on format
if size_format == "geomorphology":
fig_size_inches = 6.25
elif size_format == "big":
fig_size_inches = 16
else:
fig_size_inches = 4.92126
ax_style = "Normal"
# Get the filenames you want
BackgroundRasterName = Base_file + "_hs.bil"
ElevationName = Base_file + ".bil"
DrapeName = Drape_prefix + ".bil"
# clear the plot
plt.clf()
# set up the base image and the map
MF = MapFigure(BackgroundRasterName,
DataDirectory,
coord_type=coord_type,
colourbar_location='None')
#MF.add_drape_image(ElevationName,DataDirectory,colourmap = "gray", alpha = 0.6, colorbarlabel = None)
MF.add_binary_drape_image(DrapeName,
DataDirectory,
colourmap=cmap,
alpha=alpha)
if (use_scalebar):
print("Let me add a scalebar")
MF.add_scalebar()
# Save the image or return the figure handle
if (save_fig):
if len(out_fname_prefix) == 0:
ImageName = DataDirectory + Base_file + "_drape." + fig_format
else:
ImageName = DataDirectory + out_fname_prefix + "_drape." + fig_format
MF.save_fig(fig_width_inches=fig_size_inches,
FigFileName=ImageName,
FigFormat=fig_format,
Fig_dpi=dpi,
transparent=True)
thing_to_return = ImageName
else:
fig = MF.save_fig(fig_width_inches=fig_size_inches,
transparent=True,
return_fig=True)
thing_to_return = fig
print("I'm returning:")
print(thing_to_return)
return thing_to_return
def SimpleDrape(DataDirectory,
Base_file,
Drape_prefix,
cmap="cubehelix",
cbar_loc="right",
cbar_label="drape colourbar",
size_format="ESURF",
fig_format="png",
dpi=250,
out_fname_prefix="",
coord_type="UTM_km",
use_scalebar=False,
drape_cnorm="none",
colour_min_max=[],
discrete_cmap=False,
n_colours=10,
save_fig=True):
"""
This function makes a simple drape plot. You can choose the colourbar in this one. Similar to the PlotHillshade routine but a bit more flexible.
Args:
DataDirectory (str): the data directory with the rasters
Base_file (str): The prefix for the rasters
Drape_prefix (str): The prefix of the drape name
cmap (str or colourmap): The colourmap to use for the plot
cbar_loc (str): where you want the colourbar. Options are none, left, right, top and botton. The colourbar will be of the elevation.
If you want only a hillshade set to none and the cmap to "gray"
cbar_label (str): The text on the colourbar label
size_format (str): Either geomorphology or big. Anything else gets you a 4.9 inch wide figure (standard ESURF size)
fig_format (str): An image format. png, pdf, eps, svg all valid
dpi (int): The dots per inch of the figure
out_fname_prefix (str): The prefix of the image file. If blank uses the fname_prefix
coord_type (str): this can be in UTM_km or UTM_m
use_scalebar (bool): If true inserts a scalebar in the image
drape_cnorm (str): Sets the normalisation of the colourbar.
colour_min_max (float list): Sets the minimum and maximum values of the colourbar
discrete_cmap (bool): If true, make discrete values for colours, otherwise a gradient.
n_colours (int): number of colours in discrete colourbar
save_fig (bool): If true, saves the fig, else, returns the filename
Returns:
If save_fig is true, return a string with the name of the image (printed to file). If save_fig is false, returns the figure handle to the draped plot
Author: FJC, SMM
"""
# specify the figure size and format
# set figure sizes based on format
if size_format == "geomorphology":
fig_size_inches = 6.25
elif size_format == "big":
fig_size_inches = 16
else:
fig_size_inches = 4.92126
ax_style = "Normal"
# Get the filenames you want
BackgroundRasterName = Base_file + "_hs.bil"
ElevationName = Base_file + ".bil"
DrapeName = Drape_prefix + ".bil"
# clear the plot
plt.clf()
# set up the base image and the map
MF = MapFigure(BackgroundRasterName,
DataDirectory,
coord_type=coord_type,
colourbar_location=cbar_loc)
#MF.add_drape_image(ElevationName,DataDirectory,colourmap = "gray", alpha = 0.6, colorbarlabel = None)
MF.add_drape_image(DrapeName,
DataDirectory,
colourmap=cmap,
alpha=0.6,
colorbarlabel=cbar_label,
norm=drape_cnorm,
colour_min_max=colour_min_max,
discrete_cmap=discrete_cmap,
n_colours=n_colours)
if (use_scalebar):
print("Let me add a scalebar")
MF.add_scalebar()
# Save the image or return the figure handle
if (save_fig):
if len(out_fname_prefix) == 0:
ImageName = DataDirectory + Base_file + "_drape." + fig_format
else:
ImageName = DataDirectory + out_fname_prefix + "_drape." + fig_format
MF.save_fig(fig_width_inches=fig_size_inches,
FigFileName=ImageName,
FigFormat=fig_format,
Fig_dpi=dpi,
transparent=True)
thing_to_return = ImageName
else:
fig = MF.save_fig(fig_width_inches=fig_size_inches,
transparent=True,
return_fig=True)
thing_to_return = fig
print("I'm returning:")
print(thing_to_return)
return thing_to_return
def PrintCategorised(DataDirectory,
fname_prefix,
Drape_prefix,
show_colourbar=False,
cmap="jet",
cbar_loc="right",
cbar_label="drape colourbar",
size_format="ESURF",
fig_format="png",
dpi=250,
out_fname_prefix=""):
"""
This function makes a shaded relief plot of the DEM a drape plot that has categorised colours.
Args:
DataDirectory (str): the data directory with the m/n csv files
fname_prefix (str): The prefix for the m/n csv files
Drape_prefix (str): The prefix of the drape name
show_colourbar (bool): if true show the colourbar
cmap (str or colourmap): The colourmap to use for the plot
cbar_loc (str): where you want the colourbar. Options are none, left, right, top and botton. The colourbar will be of the elevation.
If you want only a hillshade set to none and the cmap to "gray"
cbar_label (str): The text on the colourbar label
size_format (str): Either geomorphology or big. Anything else gets you a 4.9 inch wide figure (standard ESURF size)
fig_format (str): An image format. png, pdf, eps, svg all valid
dpi (int): The dots per inch of the figure
out_fname_prefix (str): The prefix of the image file. If blank uses the fname_prefix
Returns:
A string with the name of the image (printed to file): Shaded relief plot with categorised data.
Author: SMM
"""
# set figure sizes based on format
if size_format == "geomorphology":
fig_width_inches = 6.25
elif size_format == "big":
fig_width_inches = 16
else:
fig_width_inches = 4.92126
# going to make the basin plots - need to have bil extensions.
print(
"I'm going to make a plot of categorised data. Your topographic data must be in ENVI bil format or I'll break!!"
)
# get the rasters
raster_ext = '.bil'
#BackgroundRasterName = fname_prefix+raster_ext
BackgroundRasterName = fname_prefix + '_hs' + raster_ext
CatName = Drape_prefix + raster_ext
# clear the plot
plt.clf()
# set up the base image and the map
MF = MapFigure(BackgroundRasterName,
DataDirectory,
coord_type="UTM_km",
colourbar_location=cbar_loc)
#MF.add_drape_image(ElevationName,DataDirectory,colourmap = "gray", alpha = 0.6, colorbarlabel = None)
MF.add_categorised_drape_image(CatName,
DataDirectory,
colourmap=cmap,
alpha=0.7,
colorbarlabel=cbar_label,
norm="none")
# Save the image
if len(out_fname_prefix) == 0:
ImageName = DataDirectory + fname_prefix + "_categorised." + fig_format
else:
ImageName = DataDirectory + out_fname_prefix + "_categorised." + fig_format
MF.save_fig(fig_width_inches=fig_width_inches,
FigFileName=ImageName,
FigFormat=fig_format,
Fig_dpi=dpi,
transparent=True) # Save the figure